Identification of Quantitative Trait Nucleotides and Development of Diagnostic Markers for Nine Fatty Acids in the Peanut

The cultivated peanut (Arachis hypogaea L.) is an important oilseed crop worldwide, and fatty acid composition is a major determinant of peanut oil quality. In the present study, we conducted a genome-wide association study (GWAS) for nine fatty acid traits using the whole genome sequences of 160 representative Chinese peanut landraces and identified 6-1195 significant SNPs for different fatty acid contents. Particularly for oleic acid and linoleic acid, two peak SNP clusters on Arahy.09 and Arahy.19 were found to contain the majority of the significant SNPs associated with these two fatty acids. Additionally, a significant proportion of the candidate genes identified on Arahy.09 overlap with those identified in early studies, among which three candidate genes are of special interest. One possesses a significant missense SNP and encodes a known candidate gene FAD2A. The second gene is the gene closest to the most significant SNP for linoleic acid. It codes for an MYB protein that has been demonstrated to impact fatty acid biosynthesis in Arabidopsis. The third gene harbors a missense SNP and encodes a JmjC domain-containing protein. The significant phenotypic difference in the oleic acid/linoleic acid between the genotypes at the first and third candidate genes was further confirmed with PARMS analysis. In addition, we have also identified different candidate genes (i.e., Arahy.ZV39IJ, Arahy.F9E3EA, Arahy.X9ZZC1, and Arahy.Z0ELT9) for the remaining fatty acids. Our findings can help us gain a better understanding of the genetic foundation of peanut fatty acid contents and may hold great potential for enhancing peanut quality in the future.


Introduction
The cultivated peanut (Arachis hypogaea L.) is an important oil crop worldwide, and China contributes to approximately 40% of global peanut production (http://www.fao.org,accessed on 2 June 2022).In China, the peanut accounts for half of the total oil crop production, making it the leading oil crop.Fatty acid composition is a significant factor that determines the flavor, shelf life, and nutritional quality of peanuts.In peanuts, fatty acids mainly consist of three unsaturated fatty acids (oleic acid/C18:1, linoleic acid/C18:2, and gadoleic acid/C20:1) and six saturated fatty acids (palmitic acid/C16:0, stearic acid/C18:0, arachidic acid/C20:0, behenic acid/C22:0, lignoceric acid/C24:0, and heptadecanoic acid/C17:0).Among those fatty acids, oleic acid and linoleic acid account for up to 80% of the total fatty acid content in peanuts [1].As a monounsaturated fatty acid, oleic acid is considered the most desirable fatty acid due to its potential to inhibit tumor growth, lower blood cholesterol levels, and combat inflammatory diseases [2][3][4].
Plants 2024, 13, 16 2 of 18 In plants, the de novo synthesis of fatty acids starts with acetyl-CoA, which undergoes a series of reactions to form palmitic acid (16:0), a 16-carbon saturated fatty acid [5,6].Palmitic acid is subsequently modified to produce various other fatty acids.For instance, it is first elongated to form stearic acid (C18:0), which can then be desaturated to oleic acid (C18:1) by stearoyl-acyl carrier protein desaturase in the plastids.Oleic acid may, in turn, be further desaturated to linoleic acid (C18:2), either by fatty acid desaturase 6 (FAD6) in the plastids or by FAD2 in the endoplasmic reticulum (ER).Linoleic acid can be even further desaturated into γ-linolenic acid, either by FAD3 in the ER or by FAD7/FAD8 in plastids [7].Therefore, it is possible to boost the oleic acid content of peanuts when oil is the preferred product by increasing the inflow from acetyl-CoA and/or reducing the outflow to linoleic acid.The latter can be achieved through mutations that inactivate the FAD2 desaturase [8][9][10].However, FAD2 mutants may compromise other important agronomic traits, such as stress resistance [11].Hence, discovering novel QTLs for genetic improvement is necessary, and a great deal of effort has been made [9,10,12,13].However, due to the complexity of the genetic underpinning of these quantitative traits, there is still much work to be done.Furthermore, the genetic basis of several other peanut fatty acids remains largely undiscovered.
In the present study, we focused on 160 Chinese peanut landraces with rich genetic variation [14,15] and used a gas chromatograph-mass spectrometer (GC-MS) to accurately determine their relative fatty acid contents.Based on the acquired fatty acid contents, we conducted genome-wide association studies (GWASs) to identify SNPs associated with different fatty acids, aiming to enhance our understanding of the genetic basis of peanut fatty acids.

Characterization and Distribution of SNPs in the Peanut Genome
A total of 116,443 high-quality genome-wide SNPs was obtained (Figure 1; Table S1).Most of the identified SNPs were found in intergenic regions (79.5%), while those in the exonic, intronic, and up-and downstream regions of the annotated gene account for 1.6%, 3.2%, and 15.6% of the total SNPs, respectively."A/G" is the most abundant SNP, accounting for 34.10% of the total SNPs, followed by "C/T" (33.60%)."A/C", "G/T", "A/T", and "C/G" account for 8.71%, 8.94%, 9.48%, and 5.17% of the total SNPs, respectively.

Genome-Wide Association Studies (GWASs) in Peanuts
Both the TASSEL and EMMAX results were in agreement with each other.In total, 6-1195 SNPs were significantly associated with the nine studied fatty acids (Figure 4).The majority of those SNPs were detected for oleic acid (1195) and linoleic acid (1147), while arachidic acid had the third most significant SNPs (296) (Figures 5-8 and S1; Table S2).All the other studied fatty acids had fewer than 100 significant SNPs.For oleic acid and lino-

Genome-Wide Association Studies (GWASs) in Peanuts
Both the TASSEL and EMMAX results were in agreement with each other.In total, 6-1195 SNPs were significantly associated with the nine studied fatty acids (Figure 4).The majority of those SNPs were detected for oleic acid (1195) and linoleic acid (1147), while arachidic acid had the third most significant SNPs (296) (Figures 5-8 and S1; Table S2).All the other studied fatty acids had fewer than 100 significant SNPs.For oleic acid and linoleic acid, eight clear peak SNP clusters were identified (−log 10 (p) > 6) (Figure 5).Among these clusters, two peak SNP clusters on Arahy.09(961 out of 1195) and Arahy.19 (919 out of 1147) contained the most significant SNPs associated with these two fatty acids (Table S2).

Genome-Wide Association Studies (GWASs) in Peanuts
Both the TASSEL and EMMAX results were in agreement with each other.In total, 6-1195 SNPs were significantly associated with the nine studied fatty acids (Figure 4).The majority of those SNPs were detected for oleic acid (1195) and linoleic acid (1147), while arachidic acid had the third most significant SNPs (296) (Figures 5-8 and S1; Table S2).All the other studied fatty acids had fewer than 100 significant SNPs.For oleic acid and linoleic acid, eight clear peak SNP clusters were identified (−log10(p) > 6) (Figure 5).Among these clusters, two peak SNP clusters on Arahy.09(961 out of 1195) and Arahy.19 (919 out of 1147) contained the most significant SNPs associated with these two fatty acids (Table S2).

Co-Localized Candidate Regions
Through a literature survey of early QTL mapping and GWAS studies of peanut oleic acid and linoleic acid, we found 22, 39, and 31 QTLs for oleic acid, linoleic acid, and the oleic/linoleic acid ratio, respectively (Table S3; Figure 9), which are widely distributed over 15 chromosomes.Among these QTLs, two regions on Arahy.09(113.235-115.189Mb) and Arahy.19 (155.091-155.200Mb) overlap with the two most significant peak SNP clusters identified in the present study for oleic acid and linoleic acid.Gene annotations in these two regions revealed 348 candidate genes on Arahy.09 and 56 on Arahy.19 (Table S4), among which 226 on Arahy.09 and 2 on Arahy.19 were shared with the genes annotated in the two peak SNP clusters identified in the present study for oleic acid and linoleic acid (Table S5).Among those 226 shared candidate genes on Arahy.09, 3 are of special interest: Arahy.42CZAS,Arahy.JYC97M, and Arahy.04JNDX.Arahy.04JNDX is the closest gene to one of the most significant SNPs (Chr09: 114150503) associated with linoleic acid (−log10(p) =16.86;Table 2), while the oleic/linoleic acid ratio differs dramatically between the two homozygotes at the SNPs located within Arahy.42CZAS (Chr09: 114195009) and Arahy.JYC97M (Chr09:114966251) according to the WGRS genotyping results (p values < 2.22 × 10 −16 ; Figure S2; Table 2).

Co-Localized Candidate Regions
Through a literature survey of early QTL mapping and GWAS studies of peanut oleic acid and linoleic acid, we found 22, 39, and 31 QTLs for oleic acid, linoleic acid, and the oleic/linoleic acid ratio, respectively (Table S3; Figure 9), which are widely distributed over 15 chromosomes.Among these QTLs, two regions on Arahy.09(113.235-115.189Mb) and Arahy.19 (155.091-155.200Mb) overlap with the two most significant peak SNP clusters identified in the present study for oleic acid and linoleic acid.Gene annotations in these two regions revealed 348 candidate genes on Arahy.09 and 56 on Arahy.19 (Table S4), among which 226 on Arahy.09 and 2 on Arahy.19 were shared with the genes annotated in the two peak SNP clusters identified in the present study for oleic acid and linoleic acid (Table S5).Among those 226 shared candidate genes on Arahy.09, 3 are of special interest: Arahy.42CZAS,Arahy.JYC97M, and Arahy.04JNDX.Arahy.04JNDX is the closest gene to one of the most significant SNPs (Chr09: 114150503) associated with linoleic acid (−log 10 (p) = 16.86;Table 2), while the oleic/linoleic acid ratio differs dramatically between the two homozygotes at the SNPs located within Arahy.42CZAS (Chr09: 114195009) and Arahy.JYC97M (Chr09:114966251) according to the WGRS genotyping results (p values < 2.22 × 10 −16 ; Figure S2; Table 2).Gene ontology (GO) analysis of those 226 shared candidate genes on Arahy.09were mostly found in the cellular process, metabolic process, catalytic activity, and singleorganism process.Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis showed that these 226 shared candidate genes were significantly enriched in five different pathways: protein processing in the endoplasmic reticulum, protein export, glycosaminoglycan degradation, other glycan degradation, and glycosphingolipid biosynthesis-ganglio series (Table S6).

qRT-PCR Verification
The expression patterns of seven selected candidate genes were investigated with qRT-PCR at four different kernel developmental stages (R5-R8).Three of the selected candidate genes are the three abovementioned candidate genes on chromosome Arahy.09that were detected by both earlier and the current studies for oleic acid and/or linoleic acid: Arahy.42CZAS,Arahy.JYC97M, and Arahy.04JNDX.The expression of Arahy.42CZAS(containing SNP_Chr09:114195009) increased gradually from R5 to R7.At R7, the expression of Arahy.42CZASwithin the high-oleic peanut group was, on average, three times higher than that within the low-oleic peanut group (Figure 11).Arahy.JYC97M (containing SNP_Chr09:114966251 in its coding region; missense type) had the highest expression level at the kernel developmental stage R5 in the low-oleic acid accessions.In addition, Arahy.04JNDX is the nearest gene to SNP_Chr09:114150503, and it exhibits a higher expression level at R5 and R6 than at other stages (Figure 11).

Discussion
The cultivated peanut is an important oilseed crop that is widely planted in the warm temperate, subtropical, and tropical zones between 35 • N and 35 • S [6,14].In the present study, we analyzed 160 Chinese peanut landraces representing 82.4% of the genetic variation in Chinese landraces [15,16].By combining our earlier acquired whole-genome resequencing (WGRS) data with fatty acid content data, we conducted a genome-wide association analysis (GWAS) and identified candidate genes responsible for the contents of nine fatty acids, including oleic and linoleic acids.

Known and Novel Candidate Genes Responsible for Oleic and Linoleic acid Content in Peanuts
Previous QTL/GWAS studies of peanut fatty acids mainly focused on oleic acid and linoleic acid.Many significant SNPs have been identified to be associated with these two major fatty acids, which are distributed on 15 peanut chromosomes, including Arahy.09 and Arahy.19 [6,9,10,12,13,[17][18][19][20][21][22].In the present study, most significant SNPs (>83%) for oleic acid and linoleic acid were detected within two peak SNP clusters on Arahy.09 and Arahy.19.These two SNP clusters overlap with the previously identified genomic regions for oleic and linoleic acids (Figure 5).Within the shared genomic regions on Arahy.09, the second most significant SNP_Chr9:114195009 for oleic acid is a known mutation for controlling the oleic/linoleic acid ratio in peanuts.This SNP is located within the gene Arahy.42CZAS,which codes for fatty acid desaturase 2, FAD2A.Within the FAD2A gene, the SNP_Chr09:114195009 is located at the first position of the amino acid codon for Asn150/Asp150 within the FAD2A gene, and the same A > G mutation was detected previously by Li et al. (2019) [23].FAD2 has been shown to be a key gene controlling the conversion of oleic acid to linoleic acid in the fatty acid synthesis process [24,25].In addition to Arahy.42CZAS, our study also identified novel candidate genes that may affect oleic acid content, such as the Arahy.04JNDXgene.This gene is the nearest neighbor to the most significant SNP (Chr09:114150503) for linoleic acid and has been annotated as an MYB protein-coding gene.MYB proteins are a family of DNA-binding proteins that are particularly important in the transcriptional regulation of secondary metabolism and the cell cycle.One MYB family member, MYB76, has been shown to affect seed fatty acid accumulation by affecting fatty acid synthesis, modification, degradation, and oil body formation in Arabidopsis [26].In addition, a mutation in another MYB family member, MYB89, was found to significantly promote the biosynthesis of major fatty acids in Arabidopsis seeds [27].A second novel candidate gene that was discovered to be responsible for oleic acid and linoleic acid in this study is Arahy.JYC97M.Within its coding region, we found SNP_Chr09:114966251, which involves a missense mutation (A > G).The significant oleic/linoleic acid ratio difference between the genotypes at SNP_Chr09:114966251, as identified using GWAS, have been confirmed with PARMS analysis (Figure 10; Table 3).Arahy.JYC97M codes for a JmjC domain-containing protein; the histone demethylases of the JmjC domain regulate gene transcription by changing the methylation status of arginine (R) and lysine (Q) residues and play important roles in plant growth and development [13,[28][29][30].According to our qRT-PCR analysis, the expression of Arahy.JYC97M is the highest at an early kernel developmental stage (R5) within the low-oleic-acid peanut accessions (AA), suggesting that this early overexpression of Arahy.JYC97M is likely to have activated the epigenetic regulation, which may eventually contribute to the low oleic acid content [31][32][33].However, peanut oleic and linoleic acid contents are complex quantitative traits controlled by multiple genes and influenced by the environment [34].Therefore, the performance of different peanut accessions during peanut breeding needs to consider not only genotypes but also environments and genotype-and-environment interactions.

Candidate Genes for the Other Fatty Acids
The estimated broad-sense heritabilities (0.74 < H 2 < 0.85) of the other seven studied fatty acids are high, indicating that the variation in these peanut fatty acids is primarily attributed to genetic factors.However, compared with oleic acid and linoleic acid, fewer QTL mapping/GWAS studies have explored the genetic basis of these fatty acids thus far [6,35].In the current study, we have identified novel candidate genes for those fatty acids.For example, Arahy.ZV39IJ holds the highly significant SNP for stearic acid (C18:0) and arachidic acid (C20:0).This gene codes for an oligopeptide transporter that plays a role in the transmembrane transport of plant secondary metabolites, metabolites, hormones, and other substances [36].
The candidate gene for gadoleic acid (C20:1), Arahy.F9E3EA, is the closest gene to a highly significant SNP (Chr18:47706703) for this acid.Arahy.F9E3EA encodes an Fbox/LRR protein.F-box/LRR proteins have been shown to be involved in plant growth and development, senescence, biological/abiotic stress responses, and other biological processes [37,38].
For lignoceric acid (C24:0), the identified candidate gene, Arahy.X9ZZC1, is the closest gene to the second most significant SNP (Chr08:47143843) for this acid.Arahy.X9ZZC1 codes for ethylene-responsive transcription factor (ERF) 3-like in Glycine max.ERF belongs to the AP2/ERF superfamily in plants [39], and the WRI transcription factors of the AP2/ERF superfamily have been shown to play important roles in the synthesis of fatty acids [40].For instance, WRI4 can upregulate LACS1 (long-chain acyl-CoA synthetase 1) to participate in the synthesis of long-chain fatty acids [41].The candidate gene for lignoceric acid, Arahy.U6RNCV, is the closest gene to the most significant SNP (Chr15:139394619) for this acid.Arahy.U6RNCV codes for dihydropyrimidine dehydrogenase (DPD), which is primarily involved in pyrimidine metabolism and plays a similar role in metabolizing 5-FU, a pyrimidine analog [42].

Plant Materials and Phenotype Collection
A total of 160 key peanut germplasms were cultivated at three locations (Dongying, Heze, and Laixi) in China from 2020 to 2021 (Table S7).Thirty to thirty-four individuals from each accession were planted in a two-row plot (3.00 m long and 0.80 m wide).After the harvest, the fatty acid composition of these peanut accessions was determined using gas chromatography-mass spectrometry (GC-MS).For each accession, 8-10 dry seeds were ground and sifted through a 20-mesh sieve (Shangyu Hujiang Instrument Factory, Zhejiang, China).We added 0.2 g of the acquired seed powder to a reaction mix containing a 2 mL mixture of diethyl ether and petroleum ether (1:1) (Aladdin, Shanghai, China).The reaction mix was allowed to stand for 30 min before adding 1 mL 0.4 mol/L potassium hydroxidemethanol solution (Kermel, Tianjin, China).After vortexing the reaction mix, it was left to stand for 1 h.Following this, 2 mL ultrapure water was added while ensuring the supernatant remained clear (>30 min).Finally, the reaction mix was diluted 1000 times with petroleum ether (temperature range: 60-90 • C) (Kermel, Tianjin, China).
Fatty acid composition was determined using Agilent 7890A gas chromatography (Agilent Technologies, Santa Clara, CA, USA) with an HP-88 capillary column (130 m × 0.25 mm × 0.20 µm).The carrier gas used was helium, and the column was initially set at 210 • C for 9 min, with a heating rate of 20 • C/min.The temperature was then programmed to 230 • C and maintained at this temperature for 8 min.The shunt ratio was set at 30:1, and the detector temperature was 300 • C. The hydrogen flow rate was 40 mL/min, the air flow rate was 400 mL/min, and the high-purity helium flow rate was 10 mL/min.The peak area and percentage of fatty acid composition were determined using an Agilent integrator.The fatty acid was determined by comparing the retention time with the fatty acid methyl ester standard (Sigma-Aldrich, Shanghai, China).The relative proportion of total peak area was utilized to determine the fatty acid contents.To minimize environmental effects, BLUP (best linear unbiased prediction) values were estimated for each fatty acid and used in subsequent GWAS analyses [43].The correlation coefficient between each pair of the analyzed traits was calculated with the R function "cor" (https://cran.r-project.org/bin/windows/base/,accessed on 10 March 2021), and the broad-sense heritability (H 2 ) of each trait was estimated using the R package "lem4".

Genome-Wide Association Study (GWAS)
To identify genomic loci that are significantly associated with the studied fatty acid traits, GWAS analysis was performed using TASSEL v5.2.3 and EMMAX (efficient mixed model association expedited) [44,45].A mixed linear model (MLM; Y = Xa + Qb + Ku + e, where Y denotes the phenotype, X stands for the genotypes at each SNP locus, Q represents population structure, K is the relationship between samples, and e stands for residual error) was selected for each trait.A matrix of pairwise kinship coefficients was calculated using SPAGeDi v1.5 [46].The p-value threshold for significant associations was set to 10-6.The triangular correlation heatmap was generated with LDBlockShow v2.6.3 [47].Both GO (gene ontology) enrichment analysis and KEGG (Kyoto Encyclopedia of Genes and Genomes pathway database) pathway enrichment analysis were conducted using the omicshare web server (www.omicshare.com/tools).

Literature Survey of Early QTL/GWAS Studies on Peanut Oleic and Linoleic Acids
To narrow down the candidate SNP list, we conducted a literature survey and compared our significant SNPs with QTLs detected in early QTL/GWAS studies of peanut oleic and linoleic acids [6,9,10,12,13,[17][18][19][20][21][22].The genomic regions for those earlier identified QTLs were determined using the left/right marker sequences that are publicly available (Table S3).

PARMS Genotyping
To validate the GWAS results, genotyping of nine selected SNPs (Table S8) on 160 samples was carried out with the penta-primer amplification refractory mutation system (PARMS) (Gentides, Wuhan, China) [48].Primers were designed with Primer Premier 5.0 (Table S3).After DNA extraction from each sample, PCR reactions were set in 160-well PCR plates for PARMS genotyping.Each PCR reaction well (5 µL) contained 2× PARMS PCR reaction mix, allele-specific primers (150 nM each), 400 nM locus-specific primer, and 1.4 µL of DNA template.Five microliters of mineral oil were then added into each reaction well to prevent evaporation.The thermal cycler program for PARMS started with a 15 min denaturation step at 95 • C.This was followed by 10 cycles of denaturation (95 • C for 20 s) and annealing (1 min, started at 65 • C, and then decreased 0.8 • C per cycle until reaching 57 • C).Subsequently, there were 32 cycles of denaturation at 95 • C for 20 s and annealing at 57 • C for 1 min.The 160-well PCR plates were read using a TECAN infinite M1000 plate reader.SNP calling was carried out with an online software SNPdecoder (http://www.snpway.com/snpdecoder/)combined with manual modification.In each genotyping, three main types of samples may be found: samples with homozygotes (majority), samples with heterozygotes, and samples with negative or inconclusive genotypes.The significance of the phenotypic differences between the genotypes at each SNP were detected using a Student's t-test.

qRT-PCR Verification
To investigate the expression patterns of the top SNPs, three to five high-content peanut accessions and three to five low-content accessions for each of the six of the studied fatty acids were planted in Qingdao in 2021 (Table S8).Among those peanut accessions, we included three extra-high oleic acid improved varieties (>75%), HY51, HY52, and F18, which were not included in the 160 samples for GWAS analysis.The seeds were collected from four kernel developmental stages that correspond to the R5-R8 stages as defined by Boote (1982) [49].Total RNA was extracted using the EASY spin plant RNA kit (Ailab, Beijing, China).Subsequently, all samples were treated with DNase I (Takara, Shanghai, China), and the concentration of RNA was determined using a NanoDrop ® ND-1000 (Thermo, Shanghai, China).Next, the obtained RNA was reverse transcribed into cDNA using M-MLV reverse transcriptase, and qRT-PCR for seven candidate genes was performed with the BYBR Premix Ex Taq Kit (Takara, Osaka, Japan) on a Step One system (Applied Biosystems, Carlsbad, CA, USA) (Table S9).The qRT-PCR reaction consists of an initial denaturation step at 95 • C for 10 min, followed by 40 cycles of 95 • C for 15 s and 60 • C for 30 s.The relative expression levels of each gene were calculated using the 2 −∆∆Ct method that normalized gene expression to a reference gene (Actin) with three biological replicates.

Conclusions
The peanut is a globally significant oilseed crop, and its oil quality is primarily determined by its fatty acid composition.Here in the current study, we conducted GWAS analysis of nine fatty acids, including oleic acid and linoleic acid, using the available wholegenome resequencing data.For oleic and linoleic acids, the two most significant peak SNP clusters (on Arahy.09 and Arahy.19) were found to overlap with previously identified QTLs that are responsible for oleic and linoleic acid contents.Among the candidate genes annotated from the overlapping regions, we identified both known (FAD2) and novel candidate genes.In addition, we identified candidate genes for other important fatty acids.Additionally, we also identified candidate genes for other important fatty acids.However, it is worth noting that a significant SNP does not always indicate a functional difference, and the polymorphisms or functional genes in proximity need to undergo functionality testing.Nevertheless, our results may hold great potential for future peanut oil quality improvement.

Supplementary Materials:
The following are available online at https://www.mdpi.com/article/10.3390/plants13010016/s1. Figure S1: Manhattan plots showing SNPs associated with nine different fatty acids and the oleic/linoleic ratio.For each trait, the Q-Q plots are also displayed on the right.The blue and red horizontal lines represent, respectively, the significance thresholds of −log10 (p) = 5 and/or −log10 (p) = 6.(N: Northern latitudes; E: Eastern longitude).Figure S2: Phenotypic comparison between the genotypes at each SNP based on genome-wide association study (GWAS) results.Table S1: SNP and Indel distributions on each of the 20 peanut chromosomes.Table S2: The number of significant SNPs that are identified using GWAS for each of the 9 studied fatty acids on each of the 20 peanut chromosomes.Table S3: Information table for the 90 QTLs that were identified by earlier studies.These QTLs were found to be associated with oleic acid, linoleic acid, and the oleic/linoleic ratio.S8: Primers used in the penta-primer amplification refractory mutation system (PARMS) analysis.Table S9: Information table for the 226 shared candidate genes annotated on Arahy.09.These 226 genes are shared between early studies and the current study.Table S10: High-content peanut accessions and low-content accessions for each of the six fatty acids used in qRT-PCR.

Plants 2023 , 19 Figure 1 .
Figure 1.Distribution of single-nucleotide polymorphisms (SNPs) on each of the 20 chromosomes of the cultivated peanut.The top scale indicates chromosome location (in Mb), with color representing SNP density (the number of SNPs per window).

Figure 1 .
Figure 1.Distribution of single-nucleotide polymorphisms (SNPs) on each of the 20 chromosomes of the cultivated peanut.The top scale indicates chromosome location (in Mb), with color representing SNP density (the number of SNPs per window).

Figure 2 .
Figure 2. Frequency distribution of the studied fatty acid traits.X-axis: BLUP values of the studied traits; black dotted line: kernel density plot; red line: normal distribution.

Figure 2 .
Figure 2. Frequency distribution of the studied fatty acid traits.X-axis: BLUP values of the studied traits; black dotted line: kernel density plot; red line: normal distribution.

Plants 2023 , 19 Figure 3 .
Figure 3. Correlation between the studied fatty acid traits.Dot color and size both represent the degree of correlation.These numbers represent r (coefficient of correlation) values.

Figure 3 .
Figure 3. Correlation between the studied fatty acid traits.Dot color and size both represent the degree of correlation.These numbers represent r (coefficient of correlation) values.

Figure 3 .
Figure 3. Correlation between the studied fatty acid traits.Dot color and size both represent the degree of correlation.These numbers represent r (coefficient of correlation) values.

Figure 4 .
Figure 4.The significant SNP density on each chromosome for the nine studied fatty acids.

Figure 4 .
Figure 4.The significant SNP density on each chromosome for the nine studied fatty acids.Plants 2023, 12, x FOR PEER REVIEW 6 of 19

Figure 9 .
Figure 9. QTLs identified to be associated with oleic acid, linoleic acid, and the O/L ratio.The co ful lines represent the earlier identified QTLs, and the solid triangles point to the candidate reg that are identified by the present study.O: oleic acid; L: linoleic acid; O/L: the oleic/linoleic ratio.

Figure 9 .
Figure 9. QTLs identified to be associated with oleic acid, linoleic acid, and the O/L ratio.The colorful lines represent the earlier identified QTLs, and the solid triangles point to the candidate regions that are identified by the present study.O: oleic acid; L: linoleic acid; O/L: the oleic/linoleic acid ratio.

Figure 12 .
Figure 12.Comparison of the relative gene expression levels between the high-and low-palmitic/arachidic/gadoleic/lignoceric-acid peanut accessions.The gene expression levels were obtained with

Figure 12 .
Figure 12.Comparison of the relative gene expression levels between the high-and low-palmitic/arachidic/gadoleic/lignoceric-acid peanut accessions.The gene expression levels were obtained with

Author Contributions:
Conceptualization, S.S. and J.W.; methodology, J.W., H.C. and Y.M.; software, J.W., C.Y. and Y.M.; validation, J.W., J.C. and Y.L.; formal analysis, J.W.; investigation, Q.S. and M.Y.; resources, S.S., D.S. and W.W.; data curation, J.W.; writing-original draft preparation, J.W.; writing-review and editing, J.W., Y.L. and S.S.; visualization, D.S. and W.W.; supervision, Y.L. and S.S.; project administration, S.S. and C.Q.; funding acquisition, J.W. and S.S.All authors have read and agreed to the published version of the manuscript.Funding: This work was supported by the Natural Science Foundation Project of Shandong Province (grant number ZR2021MC124), the National Key Research and Development Program of China (grant number 2022YFD1200403), the Agro-industry Technology Research System of Shandong Province (grant number SDAIT-04-02), the Qingdao Natural Science Foundation (grant number 23-2-1-44-zyydjch), and the Major Scientific and Technological Project in Xinjiang (grant number 2022A02008-3).The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.Data Availability Statement: Data are contained within the article and Supplementary Materials.

Table 1 .
Phenotypic statistics of nine peanut fatty acids under three environmental conditions.

Table 2 .
The most significant SNPs and the corresponding candidate genes for different fatty a in peanuts.

Table 2 .
The most significant SNPs and the corresponding candidate genes for different fatty acids in peanuts.

Table 3 .
Information table for the nine selected SNPs that were validated using the penta-primer amplification refractory mutation system (PARMS) analysis.Columns 5 and 6 are for the WGRS genotyping result, while columns 7-9 are for the PARMS genotyping results.

Table S4 :
Information table for the 404 candidate genes annotated from 2 genomic regions on Arahy.09(113.2-115.2Mb) and Arahy.19 (155.0-155.2Mb) that were identified with available QTL mapping and GWAS studies of peanut oleic acid and linoleic acid.Table S5: Information table for the 226 shared candidate genes annotated on Arahy.09.These 226 genes are shared between early studies and the current study.Table S6: Enriched GO pathways and Plants 2024, 13, 16 16 of 18 KEGG pathway among the 226 candidate genes identified on Arahy.09 that are shared between early studies and the current study.Table S7: Information table for the 160 studied peanut accessions.Table